Garbage Classification using Deep Learning

This project involves the development and evaluation of a deep learning model to classify garbage into different categories. The goal is to aid in effective waste management and recycling by leveraging artificial intelligence.

Overview

This project implements a Convolutional Neural Network (CNN) model to classify images of garbage into predefined categories. The model is trained, validated, and evaluated using labeled datasets.

Features

• Model Architecture: Utilizes a sequential CNN built with TensorFlow and Keras.
• Performance: Achieved an accuracy of 90.12% on the validation dataset.
• Visualization: Includes confusion matrix visualizations to analyze classification performance.
• Deployment: The trained model is saved and can be loaded for further inference.

Workflow

1. Data Preprocessing:

   • Resized and normalized image data for model input.
   • Split data into training, validation, and test sets.

2. Model Training:

   • Implemented a sequential CNN model.
   • Optimized using appropriate loss functions and optimizers.

3. Evaluation:

   • Computed accuracy metrics.
   • Visualized results using a confusion matrix.

4. Model Saving and Loading:

   • Saved the trained model using Python's pickle module.
   • Reloaded the model for inference.

Libraries and Tools

• Python 3
• TensorFlow
• Keras
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Pickle

Results

• Accuracy: The model achieved an accuracy of 98.85% on the validation dataset.
• Confusion Matrix: The confusion matrix highlights the performance across all categories, with minimal misclassifications.

How to Run

1. Clone this repository:

   git clone https://github.com/your-repo/garbage-classification.git